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Event detection, classification, and localization (ED-C-L) in an active distribution network are challenging given system dynamics but critical for taking the control actions to mitigate the impact on the system operation. In this work, a new set of algorithms are developed for ED-C-L including, a) prony based dynamic window and event detection, b) data-driven system identification (DD-SI) using Koopman Mode analysis (KMA), physics-rule, and weighted-vote based assembling technique for event classification, and c) weighted graph and KMA based localization using distribution phasor measurement unit (D-PMU) data. DD-SI is utilized to construct a transient energy matrix (TEM) and capture the system dynamics using the temporal and spatial signature of events without using a labeled dataset. The proposed technique considers system dynamics for ED-C-L to achieve enhanced accuracy, compared to most of the existing data-driven techniques. A lab-scale real-time testbed is developed to generate D-PMU data utilizing a 24 bus distribution system simulated in the OPAL-RT/Hypersim for evaluating the performance of the proposed algorithm, and measured using selected metrics. Case scenarios consider utility-scale solar photovoltaic (PV) and battery energy storage system (BESS) integration at dispersed locations to validate proposed algorithms under dynamically changing operational scenarios. Further, the developed approach is applied on DPMU data from real distribution systems with DERs to showcase the superiority of the proposed approach to field systems.

Integrating sustainable energy resources transforms the distribution grid into an active system with higher variations observed in load and generation. Estimating distributed generation, gross load, and cold load pick-up (CLPU) become more challenging with behind-the-meter (BTM) distributed energy resources (DERs), especially in case of outages caused by extreme events. This work proposes a resilience-driven restoration scheme using the most updated information from an integrated and enhanced situational awareness tool (ESAT) using kernelized Bayesian state-space inference (KBSI) with Markov Chain Monte Carlo (MCMC) and multiple optimization algorithms. ESAT consists of the BTM load/ DER estimation and disaggregation, CLPU estimation, and network topology estimation with de-energized islands. The proposed work provides solutions to establish informed restoration schemes considering resilience criteria for a quick recovery of high-priority loads. A resilience metric is utilized after outages to measure the effectiveness of ESAT-driven restoration for the supposed threats. The performance of developed ESAT is demonstrated using actual field datasets and validated using the emulated real scenarios on a benchmark model.

A method for accurate estimation of phasors for M class phasor measurement units (PMUs) is presented in this work. The M class PMUs require the filtering of the aliased signal. For this, the model order estimation of the signal is carried out for different data windows and sampling frequencies. The obtained model order is used to estimate the fundamental phasor, the first and the second derivatives of amplitude and phase of the signal using the second-order Taylor Series approximated total least square estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) method. The derivatives of the amplitude and the phase obtained, in this work, use a single data window of multiple cycle lengths. The estimated total vector error (TVE), frequency error (FE), and rate of change of frequency errors (RFE) are evaluated in the presence of noise for various test cases to comply with the synchrophasor standard and also compared with some of the recent work reported in the literature.

Cerebral phaeohyphomycosis (CP) stands as an exceedingly uncommon yet severe type of fungal infection affecting the central nervous system, attributable to dematiaceous fungi. Despite the patient's immune status, CP is associated with grave prognosis. In the present study, authors describe the first case of left thalamic fungal abscess due to Rhinocladiella mackenzieiin an immunocompetent 39-year-old male patient in Jaipur, Rajasthan. Early diagnosis by direct microscopy of aspirated pus and extensive management with surgical excision and prolonged antifungal coverage showed favourable outcome. The present case is one of the few cases documented globally who has survived.

Natural disasters and accidents have underscored the need for operative solutions that can enhance the resiliency of power distribution network (PDN). This article proposes novel methods of scheduling of battery energy storage system (BESS) and loads of microgrid to enhance the resiliency of PDN. A mixed-integer linear programming model is developed to minimize the energy mismatch between available resources and critical loads during outage period. The proposed model is aimed at minimizing the shedding of high critical loads (hcl) and maximizing the serving time of hcl during outages. In order to increase the longevity of the load serving, the fuzzy-based approach is developed to control the C_rate of the BESS. Moreover, to increase the resiliency of hcl, partitioning of BESS capacity is proposed, which provides more flexibility for charging/discharging control of the BESS. The concept of variable dispatch interval is introduced to reduce the frequent switching of loads. By considering the reactive power, an apparent power resiliency metric is proposed to quantify the resiliency of PDN. The IEEE 33-bus network is tested under various restoration periods in order to verify the performance of the proposed methodology in the MATLAB environment.

The computing infrastructure to support the power grid monitoring and control evolves with the increasing system complexity and integration of distributed energy resources (DERs). The integration of DERs requires a stability assessment application to monitor and control at a faster rate compared to the legacy system. Distributed computing architecture provides opportunities for managing these key changes by introducing solutions within substation computers and digital devices such as phasor measurement unit. The proposed decentralized voltage stability using a distributed coordination platform is a secondary emergency control mechanism utilizing large amounts of data in real-time for faster monitoring and control with the legacy system. The developed algorithms have been validated using the IEEE 30, 57, 118, and 300 bus systems using offline and real-time hardware-in-the-loop testbed modeling and simulation. To demonstrate the flexibility of the distributed coordination blocks, multiple case studies and implementation are presented. With control decisions required under changing operating conditions, the IEEE 30 bus system is simulated in real-time using a digital simulator and a network simulator (NS-3). The impact of the SYN flood cyberattack and tolerance of the computing infrastructure to resulting missing data have also been illustrated.

Filariasis of the breast is not uncommon, particularly in patients hailing from endemic regions. Awareness of the existence of the entity and recognition of characteristic imaging features are essential for appropriate care of patients.

RORγt is an important nuclear receptor that regulates the production of several pro-inflammatory cytokines such as IL-17 and IL-22. As a result, RORγt has been identified as a potential target for the treatment of various immunological disorders such as psoriasis, psoriatic arthritis, and inflammatory bowel diseases. Structure and computer-assisted drug design led to the identification of a novel series of tricyclic RORγt inverse agonists with significantly improved in vitro activity in the reporter (Gal4) and human whole blood assays compared to our previous chemotype. Through careful structure activity relationship, several potent and selective RORγt inverse agonists have been identified. Pharmacokinetic studies allowed the identification of the lead molecule 32with a low peak-to-trough ratio. This molecule showed excellent activity in an IL-2/IL-23-induced mouse pharmacodynamic study and demonstrated biologic-like efficacy in an IL-23-induced preclinical model of psoriasis.

An in silicomodel for the 1:1 ferredoxin (Fd)/nitrate reductase (NR) complex, using the known structure of Synechocystissp. PCC 6803 Fd and the in silicomodel of Synechococcussp. PCC 7942 NR, is used to map the interaction sites that define the interface between Fd and NR. To test the electrostatic interactions predicted by the model complex, five positively charged NR amino acids (Arg43, Arg46, Arg197, Lys201, and Lys614) and a negatively charged amino acid (Glu219) were altered using site-directed mutagenesis and characterized by activity measurements, metal analysis, and electron paramagnetic resonance (EPR) studies. All of the charge replacement variants retained wild-type levels of activity with reduced methyl viologen (MV), but a significant decrease in activity was observed for the R43Q, R46Q, K201Q, and K614Q variants when reduced Fd served as the electron donor. EPR analysis as well as the Fe and Mo analyses showed that loss of activity observed with these variants was not the consequence of perturbation of the Mo center or [4Fe-4S] cluster. Therefore, the loss of the Fd-linked specific activity observed with these variants can be explained only by invoking a role for Arg43, Arg46, Lys201, and Lys614 in Fd binding. The R43Q, R46Q, K201Q, and K614Q NR variants also showed a decreased binding affinity for Fd, compared to that of wild-type NR, supporting a key role of these four positively charged residues in the productive binding of Fd.

Herein, we report the synthesis and characterization of hierarchical porous activated nitrogen-doped carbon from pyrolysis of dried marigold flower precursor as an effect of different impregnation zinc chloride (ZnCl2) (1:1, 1:3 and 1:5) named FZ11, FZ13, and FZ15. BET and FESEM show the enhancement in porosity and surface area with activation with highest specific surface area of 1274.45m2/g for FZ15. XPS confirms the engineering of nitrogen content into functional nitrogen doped sites upon the action of ZnCl2activation as N-reservoir which results in increase in specific capacitance of fabricated symmetric cell (without any conductive black) increases with activation and shows the highest specific capacitance of 179 F/g, energy density of 23 Wh/kg and power density of 556 W/kg for FZ15 sample. Addition of carbon black helps in lowering ESR to 0.77 Ω and enhancement in energy density and series combination of 3 such cells glow yellow LED for ~3 min.

Early-stage detection of breast cancer is the primary requirement in modern healthcare as it is the most common cancer among women worldwide. Histopathology is the most widely preferred method for the diagnosis of breast cancer, but it requires long processing time and involves qualitative assessment of cancer by a trained person/doctor. Here, we present an alternate technique based on white light interference microscopy (WLIM) and Raman spectroscopy, which has the capability to differentiate between cancerous and normal breast tissue. WLIM provides quantitative phase information about the biological tissues/cells, whereas Raman spectroscopy can detect changes in their molecular structure and chemical composition during cancer growth. Further, both the techniques can be implemented very quickly without staining the sample. The present technique is employed to perform ex vivo study on a total of 80 normal and cancerous tissue samples collected from 16 different patients. A generalized machine learning model is developed for the classification of normal and cancerous tissues, which is based on texture features obtained from phase maps with an accuracy of 90.6%. The correlation of outcomes from these two techniques can open a new avenue for fast and accurate detection of cancer without any trained personnel.

In breast cancer, 20%–30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 freshly excised breast tissue [44 normal and 44 cancers (invasive ductal carcinoma tissues)] samples from 22 patients was used in our study. The algorithm successfully classifies the cancerous tissue with sensitivity, specificity, and accuracy of 91.56%, 93.86%, and 92.71% respectively. The present computational technique is fast, simple, and sensitive, and extracts features from the whole volume of the tissue, which does not require any special tissue preparation nor an expert to analyze the breast cancer as required in histopathology. Diagnosis of breast cancer by extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for cancer detection and would be a valuable tool for a fine-needle-guided biopsy.

Operation and management of unbalanced distribution network (UDN) is challenging and complex for optimum utilization of active resources such as distributed generation and battery energy storage systems (BESSs). In order to exploit the inherent unbalancing of UDNs as an advantage in exploiting the most benefits from BESS, a phase-sensitive day-ahead dispatch strategy for BESS is proposed. The objectives incorporated in the proposed multiobjective, multiperiod optimization include a) maximizing peak shaving, b) improving voltage profile, c) loss minimization, d) reducing tap changer operations of voltage regulators, and e) minimizing discharge–recharge cycles. A two-stage fuzzy multiobjective optimization platform is developed to 1) represent uncertainty in load demand, 2) incorporate a human-friendly linguistic interface, and 3) provide flexibility to operator for changing priorities as a function of time. A novel application of max-average and max-product compositions in fuzzy systems is employed for multiperiod optimization. The effectiveness of the proposed approach is tested on an IEEE 37-node standard test feeder with comparative results of phase-balanced and phase-sensitive BESS dispatch. It is further validated on a feeder in Northwest USA with an existing BESS. Single-objective and multiobjective optimization results with multiple Pareto solutions demonstrate the stability and robustness of the proposed approach.

HistoryA 44-year-old woman who was a resident of Bihar, which is a state in eastern India, presented to the surgical outpatient department of our hospital with a history of gradually increasing swelling of the right breast associated with redness, pain, and itching over the past month (Fig 1). She reported a general sense of malaise and experienced episodes of chills over the past 6 months; however, she had no documented fever. There was no history of breast trauma. No history suggestive of a possible hypercoagulable state could be elicited (she was a nonsmoker, had undergone uncomplicated normal vaginal delivery 15 years earlier, was not taking oral contraceptives, and had no history to suggest past deep venous thrombosis). General physical examination findings were unremarkable. On local examination, she was found to have diffuse enlargement of the right breast. The skin over the lateral part of the breast was erythematous and showed the presence of prominent superficial veins. On palpation, few ill-defined firm mobile masses were found in the upper outer quadrant with overlying skin induration. No skin ulceration or nipple discharge was present. Few firm and discrete lymph nodes were palpable in the right axilla.Laboratory investigations showed mild anemia (hemoglobin level, 10 g/dL; normal range, 12–15 g/dL), a total leukocyte count of 14 000 cells per microliter (14 cells × 109/L) (normal range, 4500–11 000 cells per microliter [4.5–11 cells × 109/L]), a normal differential leukocyte count (74% neutrophils [normal range, 40%–80%], 24% lymphocytes [normal range, 20%–40%], and 2% eosinophils [normal range, 1%–4%]), and an erythrocyte sedimentation rate of 31 mm per hour (normal range, 0–29 mm per hour). She underwent both mammography (Fig 2) and US (Fig 3). Real-time US showed mobile structures on the series of US images obtained seconds apart (Fig 4). On the basis of the imaging findings, US-guided fine-needle aspiration cytology was performed to confirm the diagnosis, and appropriate treatment was instituted.